An Expert Vision System for Autonomous Land Vehicle Road Following.
Abstract
A production system model of problem solving is applied to the design of a vision system by which an autonomous land vehicle (ALV) navigates roads. The ALV vision task consists of hypothesizing objects in a scene model and verifying these hypotheses using the vehicle's sensors. Object hypothesis generation is based on the local navigation task, an a priori road map, and the contents of the scene model. Verification of an object hypothesis involves directing the sensors toward the expected location of the object, collecting evidence in support of the object, and reasoning about the evidence. Constructing the scene model consists of building a semantic network of object frames exhibiting component, spatial, and inheritance relationships. The control structure is provided by a set of communicating production systems implementing a structured blackboard; each production system contains rules for defining the attributes of a particular class of object frame. The combination of production system and object oriented programming techniques results in a flexible control structure able to accommodate new object classes, reasoning strategies, vehicle sensors, and image analysis techniques. Keywords: Artificial intelligence; Robotics; Image Understanding; Vision-based navigation; Computer vision processing.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1988
- Accession Number
- ADA190345
Entities
People
- Larry S. Davis
- Sven J. Dickinson
Organizations
- University of Maryland